In-depth architectural comparison of the DecisionNode and Codebase Memory MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
DecisionNode
Knowledge & Memory · Local stdio
Quality: 44/100 (Fair) | Auth: No auth required
Codebase Memory MCP
Knowledge & Memory · Local stdio
Quality: 72/100 (Great) | Auth: No auth required
Verdict Summary: Choose DecisionNode if you need specialized Knowledge & Memory tools running via a local process. Choose Codebase Memory MCP if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose DecisionNode when:
You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Primary tools included: Store decisions as JSON with fields like id, scope, status, rationale, and constraints, Embed decisions using Gemini embedding model for semantic search, CLI commands for adding, searching, editing, deprecating, and exporting decisions.
Record development decisions as structured JSON, embed as vectors via Gemini, and search semantically over MCP. Shared store across Claude Code, Cursor, Windsurf, and any MCP client. CLI + MCP server, local-only, free Gemini embedding tier.
Code-intelligence engine that indexes a repo into a persistent knowledge graph — functions, classes, call chains, HTTP routes, cross-service links. 159 languages via tree-sitter + Hybrid LSP, sub-ms structural queries, 99% fewer tokens than grep. Single static binary, zero dependencies, 100% local. npx codebase-memory-mcp
Category & Scope
Tools & Capabilities Breakdown
DecisionNode Tools (6)
Store decisions as JSON with fields like id, scope, status, rationale, and constraints
Embed decisions using Gemini embedding model for semantic search
CLI commands for adding, searching, editing, deprecating, and exporting decisions
MCP server interface exposing add, search, update, delete, list, and history actions
Local web UI showing graph, vector space, and list views of decisions
Conflict detection on similar decisions and full audit trail with source tracking
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
DecisionNode is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Codebase Memory MCP belongs to Knowledge & Memory using local stdio subprocess. Select DecisionNode when you need capabilities focused on knowledge & memory and Codebase Memory MCP when you require tools for knowledge & memory.
Index a repository into the graph. Auto-sync keeps it fresh after that.
list_projects
List all indexed projects with node/edge counts.
delete_project
Remove a project and all its graph data.
index_status
Check indexing status of a project.
search_graph
Structural, BM25, and semantic search. Page structural rows with `offset`/`limit` and ranked semantic rows independently with `semantic_offset`/`semantic_limit`.
trace_path
BFS traversal — who calls a function and what it calls (alias: `trace_call_path`). Depth 1-5.
detect_changes
Map git diff to affected symbols + blast radius with risk classification.
query_graph
Execute Cypher-like graph queries (read-only).
get_graph_schema
Node/edge counts, relationship patterns, property definitions per label. Run this first.
get_code_snippet
Read source code for a function by qualified name.